CN-122017588-A - Mobile power supply lithium battery pack fault self-diagnosis method, device, equipment, medium and program product

CN122017588ACN 122017588 ACN122017588 ACN 122017588ACN-122017588-A

Abstract

The application relates to the technical field of battery management, in particular to a self-diagnosis method, device, equipment, medium and program product for faults of a lithium battery pack of a mobile power supply. The method comprises the steps of responding to the fact that a target power supply meets a preset abnormal triggering condition, obtaining event recording data of the target power supply, respectively comparing the node recording data with baseline data of all nodes of the pre-obtained target power supply in a waveform mode, obtaining dispersion of the node recording data and the baseline data, determining a fault locating point based on the dispersion, extracting fault recording data associated with the fault locating point, processing the fault recording data based on a preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features to carry out matching analysis with a predefined fault feature library, and obtaining a fault diagnosis result, wherein the fault diagnosis result comprises a plurality of fault types matched with the fault recording data and corresponding confidence degrees. The method can improve the diagnostic and analytical capability of battery health.

Inventors

WANG PEILIN
LI ZHAOWU
LI YANHUI

Assignees

深圳市玖利源电子技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260209

Claims (10)

1. A method for self-diagnosing faults of a lithium battery pack of a mobile power supply, which is characterized by comprising the following steps: Responding to the condition that a target power supply meets a preset abnormal triggering condition, and acquiring event recording data of the target power supply, wherein the event recording data comprises node recording data of a start end, a tail end and a battery cell connecting point of a battery pack of the target power supply; Respectively carrying out waveform comparison on the node recording data and the pre-acquired baseline data of each node of the target power supply, and acquiring the dispersion degree of the node recording data and the baseline data; Determining a fault locating point in the target power supply based on the dispersion, and extracting fault recording data associated with the fault locating point; Processing the fault recording data based on a preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features and a predefined fault feature library to perform matching analysis to obtain a fault diagnosis result, wherein the fault diagnosis result comprises a plurality of fault types matched with the fault recording data and corresponding confidence degrees.
2. The method of claim 1, wherein the obtaining event recording data of the target power supply in response to the target power supply meeting a preset abnormal triggering condition comprises: Detecting a fault indication signal of the target power supply based on a preset first sampling rate, wherein the fault indication signal is a basic electric signal with the correlation degree with fault occurrence meeting a preset threshold value; When the fault indication signal meets a preset trigger feature sequence, acquiring the event recording data of the target power supply; The trigger feature sequence is a combined sequence comprising a plurality of trigger conditions, wherein the trigger conditions comprise threshold trigger conditions, change rate trigger conditions and event trigger conditions.
3. The method according to claim 2, characterized in that the method comprises: Acquiring wave recording data of each node of the target power supply based on a preset second sampling rate, and writing the wave recording data into a first memory area; When the recording data is written to the tail part of the first memory area, the recording data is written back to the head part of the first memory area through a preset circulating pointer, and the old recording data of the first memory area is covered according to a time sequence.
4. The method of claim 3, wherein the obtaining event recording data of the target power supply in response to the target power supply meeting a preset abnormal triggering condition comprises: responding to the target power supply meeting the abnormal triggering condition, and writing the wave recording data in the first memory area at the abnormal triggering moment into a second memory area; writing the wave recording data in a preset time window after the abnormal triggering moment into the second memory area, and connecting two sections of wave recording data to obtain the event wave recording data, wherein the second memory area is a nonvolatile memory.
5. The method according to any one of claims 1 to 4, wherein the step of comparing the node recording data with the pre-acquired baseline data of each node of the target power supply, before acquiring the dispersion of the node recording data and the baseline data, further comprises: Acquiring historical recording data in a preset number of battery cycle periods, cleaning the historical recording data based on preset data cleaning logic, and retaining effective sample recording data; And respectively fitting based on the sample recording data to obtain the baseline data associated with each node of the target power supply.
6. The method according to claim 1, wherein the processing the fault record data based on the preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features to perform matching analysis with a predefined fault feature library, and before obtaining a fault diagnosis result, further comprises: detecting and updating impedance parameters of each battery core of a battery pack of the target power supply based on a preset period, and updating fault characteristic waveforms in the fault characteristic library based on the impedance parameters; And carrying out matching analysis on the node waveform characteristics based on the updated fault characteristic library.
7. A mobile power lithium battery pack failure self-diagnosis device, the device comprising: The data acquisition module is used for responding to the condition that a target power supply meets a preset abnormal triggering condition to acquire event recording data of the target power supply, wherein the event recording data comprise node recording data of the beginning end and the tail end of a battery pack of the target power supply and a battery cell connecting point; the characteristic comparison module is used for respectively carrying out waveform comparison on the node recording data and the pre-acquired baseline data of each node of the target power supply, and acquiring the dispersion of the node recording data and the baseline data; the fault locating module is used for determining a fault locating point in the target power supply based on the dispersion and extracting fault recording data associated with the fault locating point; The fault diagnosis module is used for processing the fault record data based on a preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features and a predefined fault feature library to perform matching analysis to obtain a fault diagnosis result, wherein the fault diagnosis result comprises a plurality of fault types matched with the fault record data and corresponding confidence degrees.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.

Description

Mobile power supply lithium battery pack fault self-diagnosis method, device, equipment, medium and program product Technical Field The application relates to the technical field of battery management, in particular to a self-diagnosis method, device, equipment, medium and program product for faults of a lithium battery pack of a mobile power supply. Background With the popularization of portable electronic devices, such as smart phones, tablet computers, notebook computers, wearable devices, etc., the requirements of people on the cruising ability of the devices are increasing. The portable power supply, as a portable energy storage and power supply device, has become an indispensable accessory in modern life. The electric energy can be supplied to the electronic equipment at any time and any place, so that the problem of electric energy anxiety of a user in a mobile scene is greatly solved, and the application scene covers a plurality of fields such as daily commute, outdoor travel, emergency rescue and the like. The core energy storage unit of a mobile power supply typically employs a lithium ion battery or a lithium polymer battery (collectively referred to as a lithium battery pack). Compared with other types of batteries, the lithium battery pack has the remarkable advantages of high energy density, low self-discharge rate, no memory effect and the like, and is very suitable for portable products of a mobile power supply, which are sensitive to volume and weight. A complete mobile power system is generally composed of a battery cell (i.e., a lithium battery pack), a charge-discharge management circuit, a voltage conversion module, an input/output interface, and the like. The performance, safety and service life of the lithium battery pack directly determine the overall quality and user experience of the mobile power supply. However, lithium batteries, while delivering high energy densities, present their own inherent safety risks and performance decay problems. Under the abnormal conditions of overcharge, overdischarge, short circuit, high temperature or physical impact, etc., the lithium battery pack may have sharply reduced performance, bulge, liquid leakage, and even serious safety accidents such as combustion or explosion. In addition, as the number of uses increases, the battery pack inevitably ages, manifesting as an increase in internal resistance and a decrease in capacity, resulting in a reduction in the duration of the mobile power supply and a decrease in output capacity. These faults and performance degradation tend to be progressive or implicit and difficult for an average user to perceive in a timely and accurate manner during daily use. In the related art, in order to improve the safety and reliability of a mobile power supply, a fault self-diagnosis technology is introduced into a product design. Existing power management techniques typically employ a dedicated battery management chip. Such chips can monitor the voltage, current and temperature of the battery and compare with preset fixed thresholds. For example, the chip may disconnect the charging loop when it detects that the battery voltage is higher than the overcharge protection threshold, and stop the charging and discharging operation when it detects that the temperature exceeds the safety range. However, the current power failure self-diagnosis method has the following technical problems: The existing fault self-diagnosis dependent diagnosis logic is simple, the dimension is single, the monitoring and analysis of the deep health index cannot be realized, and the fault self-diagnosis dependent diagnosis logic is to be optimized. Disclosure of Invention In view of the foregoing, it is desirable to provide a mobile power supply lithium battery pack fault self-diagnosis method, apparatus, computer device, computer-readable storage medium, and computer program product that can improve the diagnostic analysis capability for battery health. In a first aspect, the application provides a self-diagnosis method for faults of a lithium battery pack of a mobile power supply. The method comprises the following steps: Responding to the condition that a target power supply meets a preset abnormal triggering condition, and acquiring event recording data of the target power supply, wherein the event recording data comprises node recording data of a start end, a tail end and a battery cell connecting point of a battery pack of the target power supply; Respectively carrying out waveform comparison on the node recording data and the pre-acquired baseline data of each node of the target power supply, and acquiring the dispersion degree of the node recording data and the baseline data; Determining a fault locating point in the target power supply based on the dispersion, and extracting fault recording data associated with the fault locating point; Processing the fault recording data based on a preset feature extraction algorithm to obtain node wavef